scholarly journals A Review on the Soil Moisture Prediction Model and Its Application in the Information System

2019 ◽  
pp. 352-364 ◽  
Author(s):  
Wengang Zheng ◽  
Lili Zhangzhong ◽  
Xin Zhang ◽  
Caiyuan Wang ◽  
Shirui Zhang ◽  
...  
Keyword(s):  
Information System ◽  
Soil Moisture ◽  
Prediction Model

scholarly journals Hyperspectral parameters and prediction model of soil moisture in apple orchards

2021 ◽  
Vol 687 (1) ◽  
pp. 012085
Author(s):  
Wenqian Wang ◽  
Mingxiu Gao ◽  
Jiafan Wang
Keyword(s):  
Soil Moisture ◽  
Prediction Model ◽  
Apple Orchards

scholarly journals Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

2019 ◽  
Vol 11 (2) ◽  
pp. 125 ◽  
Author(s):  
Getachew Ayehu ◽  
Tsegaye Tadesse ◽  
Berhan Gessesse ◽  
Yibeltal Yigrem
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Prediction Model ◽  
Residual Soil ◽  
Nile Basin ◽  
Blue Nile ◽  
Volumetric Soil Moisture ◽  
Blue Nile Basin ◽  
Sar Data ◽  
Upper Blue Nile Basin

In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.

scholarly journals A note on the Hybrid Soil Moisture Deficit Model v2.0

2015 ◽  
Vol 54 (2) ◽  
pp. 126-131 ◽  
Author(s):  
Rogier P.O. Schulte ◽  
Iolanda Simo ◽  
Rachel E. Creamer ◽  
Nicholas M. Holden
Keyword(s):  
Information System ◽  
Soil Moisture ◽  
Short Note ◽  
Soil Drainage ◽  
Topographic Wetness Index ◽  
Soil Moisture Deficit ◽  
Nutrient Emissions ◽  
Moisture Deficit ◽  
Wide Range ◽  
Soil Information

Abstract The Hybrid Soil Moisture Deficit (HSMD) model has been used for a wide range of applications, including modelling of grassland productivity and utilisation, assessment of agricultural management opportunities such as slurry spreading, predicting nutrient emissions to the environment and risks of pathogen transfer to water. In the decade since its publication, various ad hoc modifications have been developed and the recent publication of the Irish Soil Information System has facilitated improved assessment of the spatial soil moisture dynamics. In this short note, we formally present a new version of the model (HSMD2.0), which includes two new soil drainage classes, as well as an optional module to account for the topographic wetness index at any location. In addition, we present a new Indicative Soil Drainage Map for Ireland, based on the Irish Soil Classification system, developed as part of the Irish Soil Information System.

A GIS Based Crash Assignment Model for Signalized T Intersections

2014 ◽  
Vol 543-547 ◽  
pp. 4472-4475
Author(s):  
Bipin Karki ◽  
Xiao Bo Qu ◽  
Kriengsak Panuwatwanich ◽  
Sherif Mohamed ◽  
Partha Parajuli
Keyword(s):  
Information System ◽  
Prediction Model ◽  
Geographical Information System ◽  
Assignment Problem ◽  
Geographical Information ◽  
Crash Prediction ◽  
Assignment Model ◽  
Gis Database ◽  
Crash Prediction Model

The crash assignment problem has long been considered as one of the most important components in an approach-level crash prediction model for intersections. A few pioneering studies have been carried out to properly assign the crashes in or nearby intersections to various approaches. However, the implementation of these models is very time consuming as it can only be done one by one manually. In this paper, a geographical information system (GIS) database is developed to complete the crash assignment. This tool has been applied in Queensland, Australia in the development of crash prediction model for signalized T-intersections.

Multi-Dimensional Grey Construction Deformation Prediction Model Research

2011 ◽  
Vol 287-290 ◽  
pp. 3116-3119
Author(s):  
Miao Chu Chen ◽  
Feng Lei Chen ◽  
Hao Zhang
Keyword(s):  
Information System ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Influence Factors ◽  
Building Construction ◽  
Grey Model ◽  
Linear Deformation ◽  
Deformation Prediction ◽  
Inspection Method ◽  
Model Research

Multi-dimensional grey model is effective in building construction poor information system with multiple influence factors. Base on its features, this paper builds multi-dimensional grey deformation prediction model to research on complex non-linear deformation system’s prediction accuracy. This paper uses the deformation data of a certain dam to perform instance demonstration, through comparative analysis, concludes the model’s data requirement, processing pattern and accuracy inspection method. The usage scope of this model is analyzed both theoretically and practically; the result proved that multi-dimensional grey model is an effective deformation prediction model.

Sign in / Sign up

Export Citation Format

Share Document